{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import scipy.io\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from __future__ import division, print_function\n",
    "\n",
    "import os\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import scipy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bbb_results = np.load('bbb_mnist_results.npz')\n",
    "implicit_results = np.load('implicit_results.npz')\n",
    "implicitR_2_results = np.load('implicitR_2_results.npz')\n",
    "mle_results = np.load('mlp_results.npz')\n",
    "dropout_results = np.load('dropout_results.npz')\n",
    "implicitR_4_results = np.load('implicitR_4_results.npz')\n",
    "implicitR_5_results = np.load('implicitR_5_results.npz')\n",
    "implicitR_6_results = np.load('implicitR_6_results.npz')\n",
    "implicit_single_gaussian_results = np.load('implicit_single_gaussian_results.npz')\n",
    "implicitR_single_gaussian_results = np.load('implicitR_single_gaussian_prior_results.npz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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K3U/jEeLiEN09iPnsCwyduoBWy86d2wns0Y1eJhO9zSZ8sU6T56rUzFOrmbZgSfwUN7Hn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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c8061d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(5,3.5))\n",
    "stride = max( int(len(mle_results['l']) / 8), 1)\n",
    "plt.plot(mle_results['l'],mle_results['nm_cdf'],'-',label='MLE', marker= 'p', color='g', markevery=stride,lw=2,  mec='k', mew=1 , markersize=10)\n",
    "plt.plot(bbb_results['l'],bbb_results['nm_cdf'],'-',label='BBB', marker= '>', color='b', markevery=stride,lw=2,  mec='k', mew=1 , markersize=10)\n",
    "plt.plot(dropout_results['l'],dropout_results['nm_cdf'],'-',label='MC Dropout', marker= 'v', color='orange', markevery=stride,lw=2,  mec='k', mew=1 , markersize=10)\n",
    "plt.plot( implicit_single_gaussian_results['l'],implicit_single_gaussian_results['nm_cdf'],'-',label='BH-Prior', marker= 'o', color='r', markevery=stride,lw=2,  mec='k', mew=1 , markersize=10)\n",
    "plt.plot( implicitR_6_results['l'],implicitR_6_results['nm_cdf'],'-',label='BH-MoG', marker= 's', color='k', markevery=stride,lw=2,  mec='k', mew=1 , markersize=10)\n",
    "# plt.plot(implicit_results['l'],implicit_results['nm_cdf'],'-',label='BH-Prior-MoG', marker= 's', color='b', markevery=stride,lw=2,  mec='k', mew=1 , markersize=10)\n",
    "\n",
    "leg = plt.legend(fontsize=16, shadow=True, loc=(0.5, -0.0))\n",
    "plt.grid('on')\n",
    "\n",
    "plt.xlabel('Entropy', fontsize=18)\n",
    "plt.ylabel('CDF', fontsize=22)\n",
    "\n",
    "\n",
    "ax = plt.gca() \n",
    "ax.yaxis.set_label_coords(-0.025, 0.5) \n",
    "ax.xaxis.set_label_coords(0.55, -0.025) \n",
    "# X-axis label\n",
    "plt.xticks((0.0, 0.5, 1.0, 1.5, 2.0), ('0.0','0.5', '', '', '2.0'), color='k', size=14)\n",
    "\n",
    "# Left Y-axis labels\n",
    "plt.yticks((0.0, 0.2, 0.4, 0.6, 0.8, 1.0), ('0.0', '','', '','','1.0'), color='k', size=14)\n",
    "\n",
    "\n",
    "\n",
    "plt.xlim(-0.05,2.2)\n",
    "\n",
    "plt.show()\n",
    "fig.savefig('hypernet_no_mnist.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
